Finding a point in time at which an object was left in a scene or taken from the scene is a very common task for video security officers and investigators. Traditionally, video management software (VMS) provides a security officer with a graphical user interface (i.e., GUI) having tools (e.g., a movable playhead, a fast forward button, rewind button, etc.) for manually searching recorded video. To search, a playhead may be moved (e.g., via a drag operation) over a graphical representation of a video stream (i.e., slider) to any point (i.e., time) in the video stream. In addition, searching may include using a fast-forward (i.e., FF) button or a rewind (i.e., RW) button to playback the video in a forward or backward direction, and in some cases, searching may include adjusting playback speed for a normal or higher (e.g., ×2, ×4, ×8, etc.) rate.
Traditional controllers may be inefficient and cumbersome for searching long video streams, especially when the current time stamp is well separated from the point (i.e., search-point) in the video stream at which the object appeared or disappeared. As a result, a user must often perform numerous control operations to hunt for the search-point. At the same time, the user must remember what time points have been searched to rule out portions of the video stream in order to find the search point. This approach may result in long searches that frustrate the user and take the user's attention away from other security duties.
Searching the video stream may be automated using video analytics. Video analytics utilize recognition algorithms that detect the characteristics (e.g., pixel level, color, shape, size, etc.) of an object and/or area to find a specified search-point. Automated searches using video analytics in a video surveillance application are complicated and computationally expensive for a variety of reasons. For example, an automated-search algorithm must accommodate changes in the lighting conditions (e.g., day/night) of an object/area during the video stream to prevent search errors. In addition, an automated-search algorithm must accommodate changes to the object (e.g., position, partial obscuration, etc.) during the video stream to prevent search errors. Further, an automated-search algorithm must be fast enough to make an automated search of a long video stream more useful than a manual search of the long video stream.
A need, therefore, exists for a system and method to search a video stream for an object's appearance or disappearance using a hybrid (i.e., interactive) approach that combines the best aspects of automated and manual searching, while mitigating the drawbacks of each.